Teacher collaborative inquiry into practice in school-based learning communities: The role of activity type
Bibliographic record
Abstract
This study contributes to growing scholarly interest in teacher-led, school-based learning communities and the characteristics of teacher dialogue and social interaction that support professional learning in these settings. Based on existing conceptual distinctions proposed in the literature, we term this type of teacher dialogue “collaborative inquiry into practice” (CLIP) and propose a systematic and reliable tool to measure it. We then employ a quantitative, comparative research design to study how different teacher team activities (i.e., video-analysis, peer consultations, and pedagogical planning) shape the extent to which teachers engage in CLIP. Fifty-four transcribed teacher meeting excerpts were analyzed with the CLIP coding scheme, assessing different aspects of inquiry-based reasoning, participation, and content. Quantitative comparisons and illustrative examples show that CLIP was lowest during peer consultations, in part because teachers were often not positioned as agents of change in such conversations. Pedagogical planning activities featured more instances of inquiry into each other's ideas. Contrary to common assumptions, collaborative video analysis activities were not characterized by increased attention to student thinking or inquiry orientation. Our findings provide new insights into teacher-led, collaborative learning in on-the-job settings, as well as practical implications for the design of school-based professional learning communities .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".